Traditional Imaging Techniques Research Articles

Automated Classification of Collateral Circulation for Ischemic Stroke in Cone-Beam CT Images Using VGG11: A Deep Learning Approach

Background: Ischemic stroke poses significant challenges in diagnosis and treatment, necessitating efficient and accurate methods for assessing collateral circulation, a critical determinant of patient prognosis. Manual classification of collateral circulation in ischemic stroke using traditional imaging techniques is labor-intensive and prone to subjectivity. This study presented the automated classification of collateral circulation patterns in cone-beam CT (CBCT) images, utilizing the VGG11 architecture. Methods: The study utilized a dataset of CBCT images from ischemic stroke patients, accurately labeled with their respective collateral circulation status. To ensure uniformity and comparability, image normalization was executed during the preprocessing phase to standardize pixel values to a consistent scale or range. Then, the VGG11 model is trained using an augmented dataset and classifies collateral circulation patterns. Results: Performance evaluation of the proposed approach demonstrates promising results, with the model achieving an accuracy of 58.32%, a sensitivity of 75.50%, a specificity of 44.10%, a precision of 52.70%, and an F1 score of 62.10% in classifying collateral circulation patterns. Conclusions: This approach automates classification, potentially reducing diagnostic delays and improving patient outcomes. It also lays the groundwork for future research in using deep learning for better stroke diagnosis and management. This study is a significant advancement toward developing practical tools to assist doctors in making informed decisions for ischemic stroke patients.

FUSION3D: Multimodal Data Fusion for 3D Shape Reconstruction - A Soft Sensing Approach

Traditional high-fidelity imaging techniques, such as X-ray computer tomography (CT), excel in capturing intricate shape details through high-resolution two-dimensional (2D) images. However, the extensive cost or impracticality of obtaining X-ray images poses a significant challenge in various applications (e.g. dense metal components in manufacturing). To overcome this limitation, we propose a soft sensing approach, named FUSION3D, aimed at reconstructing 3D shapes from 2D sliced images and heterogenous process inputs using a novel model based on multi-modal process knowledge. The developed FUSION3D method is built upon a deep learning framework that utilizes continuous normalizing flow to model the complex shape distribution of 3D objects. We propose a principled probabilistic framework to regularize 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes, and the second level is the distribution of points given a shape. Our generative regularization model learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables the computation of the likelihood during training and allows us to train our model in the variational inference framework. Our methodology is validated through a case study in additive manufacturing, demonstrating its effectiveness as a soft sensing approach for accurate 3D shape reconstruction without relying on high-fidelity imaging techniques like CT during model deployment.

Clinical Applications and Future Directions of Smartphone Fundus Imaging.

The advent of smartphone fundus imaging technology has marked a significant evolution in the field of ophthalmology, offering a novel approach to the diagnosis and management of retinopathy. This review provides an overview of smartphone fundus imaging, including clinical applications, advantages, limitations, clinical applications, and future directions. The traditional fundus imaging techniques are limited by their cost, portability, and accessibility, particularly in resource-limited settings. Smartphone fundus imaging emerges as a cost-effective, portable, and accessible alternative. This technology facilitates the early detection and monitoring of various retinal pathologies, including diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders, thereby democratizing access to essential diagnostic services. Despite its advantages, smartphone fundus imaging faces challenges in image quality, standardization, regulatory considerations, and medicolegal issues. By addressing these limitations, this review highlights the areas for future research and development to fully harness the potential of smartphone fundus imaging in enhancing patient care and visual outcomes. The integration of this technology into telemedicine is also discussed, underscoring its role in facilitating remote patient care and collaborative care among physicians. Through this review, we aim to contribute to the understanding and advancement of smartphone fundus imaging as a valuable tool in ophthalmic practice, paving the way for its broader adoption and integration into medical diagnostics.

Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN) for cross-modal individual analysis of the whole brain

Cross-modal analysis of the same whole brain is an ideal strategy to uncover brain function and dysfunction. However, it remains challenging due to the slow speed and destructiveness of traditional whole-brain optical imaging techniques. Here we develop a new platform, termed Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN), for non-destructive, high-speed, 3D imaging of ex vivo rodent, ferret, and non-human primate brains. Using an optimally designed image acquisition scheme and an accompanying machine-learning algorithm, PATTERN extracts signals of genetically-encoded probes from photobleaching-based temporal modulation and enables reliable visualization of neural projection in the whole central nervous system with 3D isotropic resolution. Without structural and biological perturbation to the sample, PATTERN can be combined with other whole-brain imaging modalities to acquire the whole-brain image with both high resolution and morphological fidelity. Furthermore, cross-modal transcriptome analysis of an individual brain is achieved by PATTERN imaging. Together, PATTERN provides a compatible and versatile strategy for brain-wide cross-modal analysis at the individual level.

Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non-demented memory clinic patients.

Plasma biomarkers of Alzheimer's disease (AD) pathology, neurodegeneration, and neuroinflammation are ideally suited for secondary prevention programs in self-sufficient persons at-risk of dementia. Plasma biomarkers have been shown to be highly correlated with traditional imaging biomarkers. However, their comparative predictive value versus traditional AD biomarkers is still unclear in cognitively unimpaired (CU) subjects and with mild cognitive impairment (MCI). Plasma (Aβ42/40, p-tau181, p-tau231, NfL, and GFAP) and neuroimaging (hippocampal volume, centiloid of amyloid-PET, and tau-SUVR of tau-PET) biomarkers were assessed at baseline in 218 non-demented subjects (CU = 140; MCI = 78) from the Geneva Memory Center. Global cognition (MMSE) was evaluated at baseline and at follow-ups up to 5.7 years. We used linear mixed-effects models and Cox proportional-hazards regression to assess the association between biomarkers and cognitive decline. Lastly, sample size calculations using the linear mixed-effects models were performed on subjects positive for amyloid-PET combined with tau-PET and plasma biomarker positivity. Cognitive decline was significantly predicted in MCI by baseline plasma NfL (β=-0.55), GFAP (β=-0.36), hippocampal volume (β = 0.44), centiloid (β=-0.38), and tau-SUVR (β=-0.66) (all p < 0.05). Subgroup analysis with amyloid-positive MCI participants also showed that only NfL and GFAP were the only significant predictors of cognitive decline among plasma biomarkers. Overall, NfL and tau-SUVR showed the highest prognostic values (hazard ratios of 7.3 and 5.9). Lastly, we demonstrated that adding NfL to the inclusion criteria could reduce the sample sizes of future AD clinical trials by up to one-fourth in subjects with amyloid-PET positivity or by half in subjects with amyloid-PET and tau-PET positivity. Plasma NfL and GFAP predict cognitive decline in a similar manner to traditional imaging techniques in amyloid-positive MCI patients. Hence, even though they are non-specific biomarkers of AD, both can be implemented in memory clinic workups as important prognostic biomarkers. Likewise, future clinical trials might employ plasma biomarkers as additional inclusion criteria to stratify patients at higher risk of cognitive decline to reduce sample sizes and enhance effectiveness.

Prefrontal Cortex Responses to Social Video Stimuli in Young Children with and without Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting individuals worldwide and characterized by deficits in social interaction along with the presence of restricted interest and repetitive behaviors. Despite decades of behavioral research, little is known about the brain mechanisms that influence social behaviors among children with ASD. This, in part, is due to limitations of traditional imaging techniques specifically targeting pediatric populations. As a portable and scalable optical brain monitoring technology, functional near infrared spectroscopy (fNIRS) provides a measure of cerebral hemodynamics related to sensory, motor, or cognitive function. Here, we utilized fNIRS to investigate the prefrontal cortex (PFC) activity of young children with ASD and with typical development while they watched social and nonsocial video clips. The PFC activity of ASD children was significantly higher for social stimuli at medial PFC, which is implicated in social cognition/processing. Moreover, this activity was also consistently correlated with clinical measures, and higher activation of the same brain area only during social video viewing was associated with more ASD symptoms. This is the first study to implement a neuroergonomics approach to investigate cognitive load in response to realistic, complex, and dynamic audiovisual social stimuli for young children with and without autism. Our results further confirm that new generation of portable fNIRS neuroimaging can be used for ecologically valid measurements of the brain function of toddlers and preschool children with ASD.

Abstract PS05-01: Accuracy and safety of fibroblast activation protein inhibitor (FAPI) PET/CT in diagnosis of axillary lymph node metastasis in early breast cancer patients: a prospective cohort study (PFB-01&amp;02 study)

Abstract Background Axillary lymph node (ALN) is the most common metastatic site of breast cancer (BC) and the status of ALN determines the staging, treatment and prognosis for BC patients. Traditional imaging techniques, including ultrasound (US), mammography, MRI, and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) were still unsatisfactory to evaluate the status of ALN non-invasively. Fibroblast activation protein (FAP) is highly expressed in cancer-associated fibroblasts and FAP inhibitor (FAPI) PET has been found quite promising in BC. Well-designed prospective large cohort study concerning the diagnostic performance and safety profile of FAPI PET/CT in detection of ALN metastasis of BC is still lacking. Furthermore, FAPI could be radio-labeled with different radio-nuclides including 68Ga and 18F, and there is yet no data concerning the 18F-labeled FAPI in BC. In this study, we investigated the accuracy of FAPI (both 68Ga-FAPI-04 and Al18F-FAPI-04) PET/CT in newly diagnosed BC patients for assessing ALN metastasis. We also compared the diagnostic performance of FAPI PET/CT with that of 18F-FDG PET/CT and US. Methods This prospective study was approved by the Peking Union Medical College Hospital Ethics Committee and was registered online at NIH ClinicalTrials.gov (NCT05574907, NCT05574920). The inclusion criteria were as follows: (1) age 18-80 years old; (2) newly diagnosed BC confirmed by biopsy or with BI-RADS category 4C or 5 lesions by US. The exclusion criteria were as follows: (1) pregnancy or lactation; (2) patients unwilling to undergo PET/CT scans. All patients received biopsy or surgery on breast lesion and ALN. Patients received FAPI and 18F-FDG PET/CT after enrollment. The safety profile of FAPI PET was monitored after injection using the Common Terminology Criteria for Adverse Events. Two experienced nuclear medicine physicians, who were blinded to the pathological diagnosis and other imaging results, assessed the PET/CT images. It was defined as visually positive if the radioactive uptake of the ALN was higher than that of the adjacent normal tissue. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each diagnostic test. The McNemar test was applied to compare the differences of sensitivity and specificity between FAPI PET/CT, 18F-FDG PET/CT, and US. All statistical tests were performed two-sided, and P&amp;lt; 0.05 was considered statistically significant. Results In total, 113 patients (27-79 years old, median age 66) were enrolled. 53 patients underwent 68Ga-FAPI-04 PET/CT, and the other 60 patients underwent Al18F-FAPI-04 PET/CT. All patients underwent 18F-FDG PET/CT and US. A total of 53/113 (46.9%) patients were finally diagnosed with ALN metastasis. The sensitivity, specificity, accuracy, PPV, and NPV were 90.6%, 90.0%, 90.3%, 88.9%, and 91.5% for FAPI PET/CT, 84.9%, 73.3%, 78.8%, 73.8%, and 84.6% for 18F-FDG PET/CT, 83.0%, 76.7%, 79.6%, 75.9%, and 83.6% for US, respectively (Table 1). The specificity of FAPI PET/CT was significantly higher than that of 18F-FDG PET/CT (P &amp;lt; 0.05) and US (P &amp;lt; 0.05). The sensitivity of FAPI PET/CT was higher than that of 18F-FDG PET/CT (P= 0.45) and US (P= 0.29). There was no significant difference of sensitivity and specificity between 18F-FDG PET/CT and US (P &amp;gt; 0.05). The sensitivity, specificity, accuracy, PPV, and NPV were 92.0%, 92.9%, 92.5%, 92.0%, and 92.9% for 68Ga-FAPI-04 PET/CT, and 89.3%, 87.5%, 88.3%, 86.2%, and 90.3% for Al18F-FAPI-04 PET/CT, respectively (Table 2). The 68Ga-FAPI-04 PET/CT and Al18F-FAPI-04 PET/CT procedures were well tolerated in all patients. The only adverse event was injection pain in 12/53 (22.6%) patients injected with 68Ga-FAPI-04, and 7/69 (10.1%) patients injected with Al18F-FAPI-04, which might relate to the ethanol in the imaging agent. Conclusion The FAPI PET/CT outperformed 18F-FDG PET/CT and US in the diagnosis of ALN metastasis among early breast cancer patients. The 68Ga-FAPI-04 and Al18F-FAPI-04 PET/CT have similar diagnostic performance. The FAPI PET/CT procedures were well tolerated in all patients. Table 1 Diagnostic performance of FAPI PET/CT, 18F-FDG PET/CT, and ultrasound in the diagnosis of ALN metastasis Table 2 Diagnostic performance of 68Ga-FAPI-04 and Al18F-FAPI-04 PET/CT in diagnosis of ALN metastasis Citation Format: Bo Pan, Zhixin Hao, Chao Ren, Lifan Zhang, Yidong Zhou, Qiang Sun, Li Huo. Accuracy and safety of fibroblast activation protein inhibitor (FAPI) PET/CT in diagnosis of axillary lymph node metastasis in early breast cancer patients: a prospective cohort study (PFB-01&amp;02 study) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS05-01.

Quantitative guided wave imaging with shear horizontal waves and deep convolutional descent full waveform inversion

Effectively determining the size and thickness distributions of corrosion damages is a vital problem in nondestructive testing (NDT). In this article, a new approach is introduced that employs magnetostrictive transducers (MST) to excite the first dispersive shear horizontal mode (SH1), and utilizes the DCD-FWI (deep convolutional descent full waveform inversion) to provide comprehensive thickness mapping in just 1 s. Compared with traditional imaging techniques, DCD-FWI network is an intrinsically full waveform inversion (FWI) based method, which yields reliable imaging results in large scale finite element simulations. Moreover, the shorter wavelength and resistance to water loading of SH guided waves bring about reliable imaging results. Combined with these advantages, the high energy transfer efficiency of the MST enhances the proposed method's robustness, as demonstrated by guided wave tomography experiments on aluminum plates.

Classification of Microscopic Hyperspectral Images of Blood Cells Based on Lightweight Convolutional Neural Network

Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitates early disease diagnosis, presenting advantages over traditional medical imaging techniques. Addressing challenges such as the computational burden of existing convolutional neural networks (CNNs) and imbalances in sample data, this paper introduces a lightweight GhostMRNet for the classification of microscopic hyperspectral images of human blood cells. The proposed model employs Ghost Modules to replace conventional convolutional layers and a cascading approach with small convolutional kernels for multiscale feature extraction, aiming to enhance feature extraction capabilities while reducing computational complexity. Additionally, an SE (Squeeze-and-Excitation) module is introduced to selectively allocate weights to features in each channel, emphasizing informative features and efficiently achieving spatial–spectral feature extraction in microscopic hyperspectral imaging. We evaluated the performance of the proposed GhostMRNet and compared it with other state-of-the-art models using two real medical hyperspectral image datasets. The experimental results demonstrate that GhostMRNet exhibits a superior performance, with an overall accuracy (OA), average accuracy (AA), and Kappa coefficient reaching 99.965%, 99.565%, and 0.9925, respectively. In conclusion, the proposed GhostMRNet achieves a superior classification performance at a smaller computational cost, thereby providing a novel approach for blood cell detection.

Active Dual Line-Laser Scanning for Depth Imaging of Piled Agricultural Commodities for Itemized Processing Lines.

The accurate depth imaging of piled products provides essential perception for the automated selection of individual objects that require itemized food processing, such as fish, crabs, or fruit. Traditional depth imaging techniques, such as Time-of-Flight and stereoscopy, lack the necessary depth resolution for imaging small items, such as food commodities. Although structured light methods such as laser triangulation have high depth resolution, they depend on conveyor motion for depth scanning. This manuscript introduces an active dual line-laser scanning system for depth imaging static piled items, such as a pile of crabs on a table, eliminating the need for conveyor motion to generate high-resolution 3D images. This advancement benefits robotic perception for loading individual items from a pile for itemized food processing. Leveraging a unique geometrical configuration and laser redundancy, the dual-laser strategy overcomes occlusions while reconstructing a large field of view (FOV) from a long working distance. We achieved a depth reconstruction MSE of 0.3 mm and an STD of 0.5 mm on a symmetrical pyramid stage. The proposed system demonstrates that laser scanners can produce depth maps of complex items, such as piled Chesapeake Blue Crab and White Button mushrooms. This technology enables 3D perception for automated processing lines and offers broad applicability for quality inspection, sorting, and handling of piled products.

Neural‐Optic Co‐Designed Polarization‐Multiplexed Metalens for Compact Computational Spectral Imaging

AbstractIn the expanding fields of mobile technology and augmented reality, there is a growing demand for compact, high‐fidelity spectral imaging systems. Traditional spectral imaging techniques face limitations due to their size and complexity. Diffractive optical elements (DOEs), although helpful in reducing size, primarily modulate the phase of light. Here, an end‐to‐end computational spectral imaging framework based on polarization‐multiplexed metalens is introduced. A distinguishing feature of this approach lies in its capacity to simultaneously modulate orthogonal polarization channels. When harnessed in conjunction with a neural network, it facilitates the attainment of high‐fidelity spectral reconstruction. Importantly, the framework is intrinsically fully differentiable, a feature that permits the joint optimization of both the metalens structure and the parameters governing the neural network. The experimental results presented herein validate the exceptional spatial‐spectral reconstruction performance, underscoring the efficacy of this system in practical, real‐world scenarios. This innovative approach transcends the traditional boundaries separating hardware and software in the realm of computational imaging and holds the promise of substantially propelling the miniaturization of spectral imaging systems.

IVR-fNIRS: studying brain functions in a fully immersive virtual environment.

Immersive virtual reality (iVR) employs head-mounted displays or cave-like environments to create a sensory-rich virtual experience that simulates the physical presence of a user in a digital space. The technology holds immense promise in neuroscience research and therapy. In particular, virtual reality (VR) technologies facilitate the development of diverse tasks and scenarios closely mirroring real-life situations to stimulate the brain within a controlled and secure setting. It also offers a cost-effective solution in providing a similar sense of interaction to users when conventional stimulation methods are limited or unfeasible. Although combining iVR with traditional brain imaging techniques may be difficult due to signal interference or instrumental issues, recent work has proposed the use of functional near infrared spectroscopy (fNIRS) in conjunction with iVR for versatile brain stimulation paradigms and flexible examination of brain responses. We present a comprehensive review of current research studies employing an iVR-fNIRS setup, covering device types, stimulation approaches, data analysis methods, and major scientific findings. The literature demonstrates a high potential for iVR-fNIRS to explore various types of cognitive, behavioral, and motor functions in a fully immersive VR (iVR) environment. Such studies should set a foundation for adaptive iVR programs for both training (e.g., in novel environments) and clinical therapeutics (e.g., pain, motor and sensory disorders and other psychiatric conditions).

245 Linking Cervical Spinal Cord White Matter Magnetization Transfer Ratio to NIH Toolbox Based Analyses of Motor Function in Degenerative Cervical Myelopathy

INTRODUCTION: Traditional spine imaging technique present disparity between degenerative cervical myelopathy (DCM) disease severity and prognosis. Advancement in spine neuroimaging has unveiled key changes that enhance our understanding of DCM pathogenesis. We investigated region- and tract-specific SC white matter (WM) damage and their relationship to motor deficit in DCM. METHODS: A cohort of 20 DCM and 23 age-matched HC participants were recruited in a cross-sectional study. We utilized a consensus MT-imaging protocol, Spinal cord Toolbox, and objective motor assessment tool battery to assess the extent of WM injury and functional deficits in DCM. The SC WM region- and tract-based MTR were calculated in three different ROIs: maximum compressed level (MCL), caudal uncompressed level (C7), and central region of compression. RESULTS: DCM patients demonstrated significant functional deficits compared to HC in motor assessments. DCM exhibited reduced MTR measures denoting WM injury in the anterior region of the SC and within selected motor tracts in the ROIs. MTR values in reticulospinal, vestibulospinal, and lateral corticospinal tracts were significantly associated with dexterity and grip strength measures. CONCLUSIONS: Our methodology enables precise mapping of ROIs within the SC allowing us to demonstrate an association between tract-specific WM damage and functional motor deficits in DCM patients. Additionally, the observation of decreased MTR measures in seemingly uncompressed SC regions indicates the pathophysiology of DCM that may provide insight into the varying presentations of DCM. Identification of specific WM injury may help better understand DCM deficits that can be targeted for effective interventions to improve patient outcomes

Optical Imaging Method of Synthetic-Aperture Radar for Moving Targets

Traditional synthetic-aperture radar (SAR) imaging, while adept at capturing stationary scenes with clarity, often results in the blurring of moving targets due to Doppler spectrum disparities. To overcome this, this study introduces an innovative optical approach for imaging moving targets. By employing a spatial light modulator (SLM), the phase information of SAR data can be modulated onto a light beam. Then, the light propagation path is meticulously designed to take advantage of the free propagation of light to compensate for the phase differences in SAR data, thereby achieving focal clarity. Simulations and experimental validations have demonstrated that this method surpasses traditional digital imaging techniques in terms of focusing precision. Additionally, the processing delay is only as long as the time taken for light propagation. The optical path is simple, avoiding complex assembly and alignment. This novel approach opens up new possibilities for the SAR imaging of moving targets, offering potential applications in moving target extraction, separation, and velocity estimation.

PODB: A learning-based polarimetric object detection benchmark for road scenes in adverse weather conditions

Due to its insensitivity to light intensity and the capability to capture multidimensional information, polarimetric imaging technology has been proven to have advantages over traditional intensity-based imaging techniques for object detection tasks in adverse environmental conditions, particularly in road traffic scenarios. Recently, with the rapid development of artificial intelligence technology, deep learning (DL)-powered object detection techniques can further enhance recognition accuracy and algorithm robustness. This improvement is made possible by the ability of DL technology to leverage large datasets and extract deeper levels of target-specific features. However, constructing large-scale polarimetric datasets poses challenges as obtaining polarimetric information requires multiple captures of intensity images. In other words, the workload is several times higher compared to traditional imaging techniques. To address the current scarcity of polarimetric datasets and evaluate the practical performance of various algorithms on polarimetric datasets, this paper proposes a Polarimetric Object Detection Benchmark (PODB) dataset. The PODB provides an integrated quality evaluation framework for DL-based object detection algorithms in complex road scenes by incorporating polarimetric imaging. Besides, we conducted extensive object detection experiments using the PODB, which allowed for a comprehensive validation and performance evaluation of effective benchmark algorithms. Furthermore, a physics-based multi-scale image fusion cascaded object detection neural network model is proposed. By combining the multidimensional information provided by polarized images with an adaptive learning multi-decision object detection neural network model, the recognition accuracy of complex road scenes in adverse weather conditions has been improved by approximately 10%. Additionally, we anticipate that PODB will serve as an effective platform for evaluating and comparing the performance of object detection algorithms, as well as providing researchers with a baseline for future studies in developing new DL-based methods.

Brain Tumor Detection: Transfer Learning Approach with VGG-16

Brain tumors have a profound impact on individuals worldwide, necessitating early detection to improve patient outcomes. This study explores the potential of deep learning in facilitating early tumor detection to aid medical professionals in timely intervention and personalized treatment strategies. While current tumor detection methodologies rely on traditional imaging techniques and manual analysis, they may suffer from limitations in accuracy and efficiency. To address these challenges, we propose a novel method using the VGG-16 architecture, a powerful Convolutional Neural Network (CNN) model, with pre-trained weights for feature extraction. By harnessing the capability of VGG-16, our model effectively discerns tumor patterns from brain MRI scans. The dataset utilized for training and evaluation consists of Brain MRI Images for Brain Tumor Detection from Kaggle, featuring a diverse range of annotated scans. During the evaluation, we focus on the primary performance metric of accuracy, which measures the model's ability to precisely classify tumor and non-tumor cases. Through extensive experimentation and analysis, we critically assess the model's performance and its potential clinical applicability

Clinical Values of Whole-Body Blood Pool Scans and SPECT for Evaluation of Congenital Vascular Malformation.

This study examines the diagnostic potential of whole-body blood pool scintigraphy (WBBPS) using technetium-99m-labeled red blood cells to detect congenital vascular malformations (CVMs). It aims to compare its efficacy with traditional imaging techniques such as magnetic resonance imaging (MRI) and ultrasonography (USG), emphasizing its potential advantages in terms of characterization of lesions and capacity for whole-body assessment. The efficacy of WBBPS and single-photon emission computed tomography (SPECT)/computed tomography (CT) imaging in diagnosing CVMs, comparing them with USG and MRI results, was evaluated in this retrospective study. Of the 38 patients, 21 were evaluated using these diagnostic methods, with CVMs classified according to the International Society for the Study of Vascular Anomalies guidelines. Also, this study aimed to elucidate the characteristics between WBBPS, SPECT/CT, USG, or MRI findings and their consistency with the final diagnosis. A total of 21 participants were included in this study, with an average age of 17.7 years old, with female predominance (57.1%). The most common diagnosis was vascular malformations (VMs) (71.4%), followed by combined vascular malformations (14.3%) and lymphatic malformations (9.5%). WBBPS demonstrated positive results in 95.2% of cases. Distinct imaging patterns for each condition were observed, with WBBPS being crucial in locating lesions. The study findings suggested that WBBPS with SPECT/CT could be helpful in detecting occult VM lesions and ruling out a lymphatic malformation diagnosis. Thus, it can be employed in the evaluation of CVMs.

The application of radionuclide therapy for breast cancer

Radionuclide-mediated diagnosis and therapy have emerged as effective and low-risk approaches to treating breast cancer. Compared to traditional anatomic imaging techniques, diagnostic radionuclide-based molecular imaging systems exhibit much greater sensitivity and ability to precisely illustrate the biodistribution and metabolic processes from a functional perspective in breast cancer; this transitions diagnosis from an invasive visualization to a noninvasive visualization, potentially ensuring earlier diagnosis and on-time treatment. Radionuclide therapy is a newly developed modality for the treatment of breast cancer in which radionuclides are delivered to tumors and/or tumor-associated targets either directly or using delivery vehicles. Radionuclide therapy has been proven to be eminently effective and to exhibit low toxicity in eliminating both primary tumors and metastases, and even undetected tumors. In addition, the specific interaction between the surface modules of the delivery vehicles and the targets on the surface of tumor cells enables radionuclide targeting therapy, and this represents an exceptional potential for this treatment in breast cancer. This article reviews the development of radionuclide molecular imaging techniques that are currently employed for early breast cancer diagnosis and both the progress and challenges of radionuclide therapy employed in breast cancer treatment.

Peripheral retinal lesions in diabetic retinopathy on ultra-widefield imaging.

Peripheral retinal imaging plays a crucial role in the diagnosis, management, and prognosis of diabetic retinopathy (DR). Traditional fundus imaging techniques have limited coverage of the retina, resulting in missed peripheral lesions. The advent of ultra-widefield (UWF) imaging has revolutionized the assessment of the peripheral retina. UWF imaging modalities provide comprehensive visualization of the retina, enabling the detection of peripheral lesions without the need for mydriasis. Integration of UWF imaging with other modalities, including fluorescein angiography (FA), indocyanine green angiography, pseudocolor imaging, and fundus autofluorescence, further enhances our understanding of peripheral retinal lesions. UWF imaging has demonstrated improved detection of DR lesions and presumably more accurate management of DR compared to traditional fundus photography and dilated fundus examination. UWF-FA and UWF-optical coherence tomography angiography have emerged as valuable tools for assessing retinal and choroidal vascular abnormalities, nonperfusion areas, neovascularization, and microvascular abnormalities. The presence and increasing extent of predominantly peripheral lesions detected using UWF FA are associated with a higher risk of DR progression and proliferative DR. UWF imaging provides a comprehensive evaluation of DR severity, aiding in more accurate risk stratification and treatment decision-making. Overall, UWF imaging modalities have significantly advanced our understanding of peripheral retinal lesions in DR, facilitating early detection and targeted management for better visual outcomes.

NIR-II fluorescence imaging in liver tumor surgery: A narrative review

In liver tumor surgery, the recognition of tumor margin and radical resection of microcancer focis have always been the crucial points to reduce postoperative recurrence of tumor. However, naked-eye inspection and palpation have limited effectiveness in identifying tumor boundaries, and traditional imaging techniques cannot consistently locate tumors in real time. As an intraoperative real-time navigation imaging method, NIR fluorescence imaging has been extensively studied for its simplicity, reliable safety, and superior sensitivity, and is expected to improve the accuracy of liver tumor surgery. In recent years, the research focus of NIR fluorescence has gradually shifted from the first near-infrared window (NIR-I, 700–900 nm) to the second near-infrared window (NIR-II, 1000–1700[Formula: see text]nm). Fluorescence imaging in NIR-II reduces the scattering effect of deep tissue, providing a preferable detection depth and spatial resolution while significantly eliminating liver autofluorescence background to clarify tumor margin. Developing fluorophores combined with tumor antibodies will further improve the precision of fluorescence-guided surgical navigation. With the development of a bunch of fluorophores with phototherapy ability, NIR-II can integrate tumor detection and treatment to explore a new therapeutic strategy for liver cancer. Here, we review the recent progress of NIR-II fluorescence technology in liver tumor surgery and discuss its challenges and potential development direction.